Monetizing shipping emission reduction: Environmental benefit analysis of domestic emission control areas policy 2.0 in China.

Shipping is a major contributor to anthropogenic emissions, exerting complex effects on both the environment and climate. To improve the air quality of coastal areas, China adopted an upgraded policy on domestic emission control areas (DECA 2.0) for shipping in December 2018, which expanded the geographical scope of ECAs from three designated heavy-traffic coastal regions to the entire 12 nautical mile-territorial seas and also introduced more stringent ship emission requirements. Based on data from the Automatic Identification System, this study first evaluates the environmental effects of ship emissions' reduction brought by DECA policy 2.0. Results reveal that implementation of DECA policy 2.0 has resulted in a cumulative reduction (2019-2021) of 8.43 × 105 tons, 1.3 × 105 tons, and 1.49 × 105 tons of sulfur dioxide (SO2), particulate matter <2.5 µm diameter (PM2.5) and PM <10 µm (PM10) emissions, respectively. Based on the external cost method, we further monetize the environmental benefits arising from reduction of air pollution emissions, averaging $3.6 billion USD per year. This number equates to ca. 4 % of the total output value of China's marine transportation industry over the three-year period. Finally, we calculate the fuel replacement cost arising from the implementation of DECA policy 2.0, which is on average $1.23 billion USD. This indicates that the net environmental benefits of DECA policy 2.0 equate nearly double the associated costs.

A SAR Ship Detection Method Based on Adversarial Training.

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods-for example, clipping, pasting, and mixing-are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods.

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks.

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

Helicobacter pylori East Asian type CagA hijacks more SHIP2 by its EPIYA-D motif to potentiate the oncogenicity.

CagA is a significant oncogenic factor injected into host cells by Helicobacter pylori, which is divided into two subtypes: East Asian type (CagAE), characterized by the EPIYA-D motif, and western type (CagAW), harboring the EPIYA-C motif. CagAE has been reported to have higher carcinogenicity than CagAW, although the underlying reason is not fully understood. SHIP2 is an intracellular phosphatase that can be recruited by CagA to perturb the homeostasis of intracellular signaling pathways. In this study, we found that SHIP2 contributes to the higher oncogenicity of CagAE. Co-Immunoprecipitation and Pull-down assays showed that CagAE bind more SHIP2 than CagAW. Immunofluorescence staining showed that a higher amount of SHIP2 recruited by CagAE to the plasma membrane catalyzes the conversion of PI(3,4,5)P3 into PI(3,4)P2. This alteration causes higher activation of Akt signaling, which results in enhanced IL-8 secretion, migration, and invasion of the infected cells. SPR analysis showed that this stronger interaction between CagAE and SHIP2 stems from the higher affinity between the EPIYA-D motif of CagAE and the SH2 domain of SHIP2. Structural analysis revealed the crucial role of the Phe residue at the Y + 5 position in EPIYA-D. After mutating Phe of CagAE into Asp (the corresponding residue in the EPIYA-C motif) or Ala, the activation of downstream Akt signaling was reduced and the malignant transformation of infected cells was alleviated. These findings revealed that CagAE hijacks SHIP2 through its EPIYA-D motif to enhance its carcinogenicity, which provides a better understanding of the higher oncogenic risk of H. pylori CagAE.

A streamlined approach for intelligent ship object detection using EL-YOLO algorithm.

Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

Coexistence of roof rats and carnivores in barns on a livestock farm in Japan.

Brown rats (Rattus norvegicus), roof rats (Rattus rattus), and house mice (Mus musculus) are considered to be important pests on livestock farms. Although the diel activity patterns of rodents are key to their control, information on this aspect of their ecology is limited. Furthermore, the effect of carnivores on rodent activity patterns as well as the carnivore species present on livestock farms is unclear. Here, we set camera traps in an open-type cow barn and in an enclosed pig barn on the same livestock farm in Japan from August through October 2021. The only rodents observed in both barns were roof rats, and the carnivore species observed were dogs (Canis familiaris), cats (Felis catus), and Japanese weasels (Mustela itatsi). Roof rats showed different patterns of activity and behavior between the barns. However, because the pattern in both barns was nocturnal, the activity patterns of roof rats and carnivores showed a moderate to high degree of overlap. Therefore, roof rats did not appear to shift their activity patterns to avoid nocturnal carnivores. Taken together, the present study provides valuable information for rodent control on livestock farms in Japan.

Ikaros sets the threshold for negative B-cell selection by regulation of the signaling strength of the AKT pathway.

Inhibitory phosphatases, such as the inositol-5-phosphatase SHIP1 could potentially contribute to B-cell acute lymphoblastic leukemia (B-ALL) by raising the threshold for activation of the autoimmunity checkpoint, allowing malignant cells with strong oncogenic B-cell receptor signaling to escape negative selection. Here, we show that SHIP1 is differentially expressed across B-ALL subtypes and that high versus low SHIP1 expression is associated with specific B-ALL subgroups. In particular, we found high SHIP1 expression in both, Philadelphia chromosome (Ph)-positive and ETV6-RUNX1-rearranged B-ALL cells. As demonstrated by targeted knockdown of SHIP1 by RNA interference, proliferation of B-ALL cells in vitro and their tumorigenic spread in vivo depended in part on SHIP1 expression. We investigated the regulation of SHIP1, as an important antagonist of the AKT signaling pathway, by the B-cell-specific transcription factor Ikaros. Targeted restoration of Ikaros and pharmacological inhibition of the antagonistic casein kinase 2, led to a strong reduction in SHIP1 expression and at the same time to a significant inhibition of AKT activation and cell growth. Importantly, the tumor suppressive function of Ikaros was enhanced by a SHIP1-dependent additive effect. Furthermore, our study shows that all three AKT isoforms contribute to the pro-mitogenic and anti-apoptotic signaling in B-ALL cells. Conversely, hyperactivation of a single AKT isoform is sufficient to induce negative selection by increased oxidative stress. In summary, our study demonstrates the regulatory function of Ikaros on SHIP1 expression in B-ALL and highlights the relevance of sustained SHIP1 expression to prevent cells with hyperactivated PI3K/AKT/mTOR signaling from undergoing negative selection.

Split_ Composite: A Radar Target Recognition Method on FFT Convolution Acceleration.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT's inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition.

Finer Particle Size Distribution and Potential Higher Toxicity of Elemental Carbon and Polycyclic Aromatic Hydrocarbons Emitted by Ships after Fuel Oil Quality Improvement.

Ship emissions are a significant source of air pollution, and the primary policy to control is fuel oil quality improvement. However, the impact of this policy on particle size distribution and composition characteristics remains unclear. Measurements were conducted on nine different vessels (ocean-going vessels, coastal cargo ships, and inland cargo ships) to determine the impact of fuel upgrading (S < 0.1% m/m marine gas oil (MGO) vs S < 0.5% m/m heavy fuel oil (HFO)) on elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) emitted by ships. (1) Fuel improvement significantly reduced EC and PAH emission, by 31 ± 25 and 45 ± 38%, respectively. However, particle size distributions showed a trend toward finer particles, with the peak size decreasing from DP = 0.38-0.60 µm (HFO) to DP = 0.15-0.25 µm (MGO), and the emission factor of DP < 100 nm increased. (2) Changes in emission characteristics led to an increase in the toxicity of ultrafine particulate matter. (3) Ship types and engine conditions affected the EC and PAH particle size distributions. Inland ships have a more concentrated particle size distribution. Higher loads result in higher emissions. (4) The composition and engine conditions of fuel oils jointly affected pollutant formation mechanisms. MGO and HFO exhibited opposite EC emissions when emitting the same level of PAHs.

Investigation of emission characteristics of a marine cargo ship in real-world conditions.

Pollution caused by ship emissions will considerably impact coastal areas. A test system that matched the actual conditions of a ship was designed based on a portable emission measurement system (PEMS), and the emission characteristic of gaseous and particle emissions and the particle size distribution of the ship's main engine were investigated under real-world operating conditions. The results showed that the emission concentrations of the main pollutants fluctuated greatly under the departure, anchoring, and docking conditions, and the peaks of CO, CO2, and NOx emissions appeared under these transient conditions. The emission concentrations of CO2, hydrocarbons, particle number (PN), and particulate mass increased with the increase in speed. The PN-based particle size distribution of the engine presented a unimodal distribution under daily operating conditions. The maximum emission factor of NOx based on the engine power was 29.53 g/kWh at the engine speed of 66 r/min. The results of the study may contribute to supplementing the emission factors of this type of ship, and provide data support for monitoring and assessment of the marine environment.

An improved sampled-data control for a nonlinear dynamic positioning ship with Takagi-Sugeno fuzzy model.

This article considered the sampled-data control issue for a dynamic positioning ship (DPS) with the Takagi-Sugeno (T-S) fuzzy model. By introducing new useful terms such as second-order term of time, an improved Lyapunov-Krasovskii function (LKF) was constructed. Additionally, the reciprocally convex method is introduced to bound the derivative of LKF. According to the constructed LKF, the sampling information during the whole sampling period was fully utilized, and less conservatism was obtained. Then, the stability condition, robust performance, mode uncertainty and sampled-data controller design were analyzed by means of the linear matrix inequality (LMI). Finally, an example was given to demonstrate the effectiveness of the proposed method.

A Compensation Method for the Geomagnetic Measurement Error of an Underwater Ship-Borne Magnetometer Based on Constrained Total Least Squares.

When magnetic matching aided navigation is applied to an underwater vehicle, the magnetometer must be installed inside the vehicle, considering the navigation safety and concealment of the underwater vehicle. Then, the interference magnetic field will seriously affect the accuracy of geomagnetic field measurement, which directly affects the accuracy of geomagnetic matching aided navigation. Therefore, improving the accuracy of geomagnetic measurements inside the vehicle through error compensation has become one of the most difficult problems that requires an urgent solution in geomagnetic matching aided navigation. In order to solve this problem, this paper establishes the calculation model of the internal magnetic field of the underwater vehicle and the geomagnetic measurement error model of the ship-borne magnetometer. Then, a compensation method for the geomagnetic measurement error of the ship-borne magnetometer, based on the constrained total least square method, is proposed. To verify the effectiveness of the method proposed in this paper, a simulation experiment of geomagnetic measurement and compensation of a ship-borne three-axis magnetometer was constructed. Among them, to be closer to the real situation, a combination of the geomagnetism model, the elliptic shell model and the magnetic dipole model was used to simulate the internal magnetic field of the underwater vehicle. The experimental results indicated that the root mean square error of geomagnetic measurement in an underwater vehicle was less than 5 nT after compensation, and the accuracy of geomagnetic measurement met the requirements of geomagnetic matching aided navigation.

Anthracycline-induced hypertension in pediatric cancer survivors: unveiling the long-term cardiovascular risks.

BACKGROUND: Long-term cardiovascular complications are common among pediatric cancer survivors, and anthracycline-induced hypertension has become an essential reason for concern. Compared to non-cancer controls, survivors have a higher prevalence of hypertension, and as they age, their incidence rises, offering significant dangers to cardiovascular health. MAIN BODY: Research demonstrates that exposure to anthracyclines is a major factor in the development of hypertension in children who have survived cancer. Research emphasizes the frequency and risk factors of anthracycline-induced hypertension, highlighting the significance of routine measurement and management of blood pressure. Furthermore, cardiovascular toxicities, such as hypertension, after anthracycline-based therapy are a crucial be concerned, especially for young adults and adolescents. Childhood cancer survivors deal with a variety of cardiovascular diseases, such as coronary artery disease and cardiomyopathy, which are made worse by high blood pressure. In order to prevent long-term complications, it is essential to screen for and monitor for anthracycline-induced hypertension. Echocardiography and cardiac biomarkers serve as essential tools for early detection and treatment. In order to lower cardiovascular risks in pediatric cancer survivors, comprehensive management strategies must include lifestyle and medication interventions in addition to survivor-centered care programs. SHORT CONCLUSION: Proactive screening, monitoring, and management measures are necessary for juvenile cancer survivors due to the substantial issue of anthracycline-induced hypertension in their long-term care. To properly include these strategies into survivor-ship programs, oncologists, cardiologists, and primary care physicians need to collaborate together. The quality of life for pediatric cancer survivors can be enhanced by reducing the cardiovascular risks linked to anthracycline therapy and promoting survivor-centered care and research.

Alteration of gene expression and protein solubility of the PI 5-phosphatase SHIP2 are correlated with Alzheimer's disease pathology progression.

A recent large genome-wide association study has identified EGFR (encoding the epidermal growth factor EGFR) as a new genetic risk factor for late-onset AD. SHIP2, encoded by INPPL1, is taking part in the signalling and interactome of several growth factor receptors, such as the EGFR. While INPPL1 has been identified as one of the most significant genes whose RNA expression correlates with cognitive decline, the potential alteration of SHIP2 expression and localization during the progression of AD remains largely unknown. Here we report that gene expression of both EGFR and INPPL1 was upregulated in AD brains. SHIP2 immunoreactivity was predominantly detected in plaque-associated astrocytes and dystrophic neurites and its increase was correlated with amyloid load in the brain of human AD and of 5xFAD transgenic mouse model of AD. While mRNA of INPPL1 was increased in AD, SHIP2 protein undergoes a significant solubility change being depleted from the soluble fraction of AD brain homogenates and co-enriched with EGFR in the insoluble fraction. Using FRET-based flow cytometry biosensor assay for tau-tau interaction, overexpression of SHIP2 significantly increased the FRET signal while siRNA-mediated downexpression of SHIP2 significantly decreased FRET signal. Genetic association analyses suggest that some variants in INPPL1 locus are associated with the level of CSF pTau. Our data support the hypothesis that SHIP2 is an intermediate key player of EGFR and AD pathology linking amyloid and tau pathologies in human AD.

Particulate Matter (PM) and Parent, Nitrated and Oxygenated Polycyclic Aromatic Hydrocarbon (PAH) Emissions of Emulsified Heavy Fuel Oil in Marine Low-Speed Main Engine.

To understand the influences of emulsified fuel on ship exhaust emissions more comprehensively, the emissions of particulate matter (PM), nitrated, oxygenated and parent polycyclic aromatic hydrocarbons (PAHs) were studied on a ship main engine burning emulsified heavy fuel oil (EHFO) and heavy fuel oil (HFO) as a reference. The results demonstrate that EHFO (emulsified heavy fuel oil) exhibits notable abilities to significantly reduce emissions of particulate matter (PM) and low molecular weight PAHs (polycyclic aromatic hydrocarbons) in the gas phase, particularly showcasing maximum reductions of 13.99% and 40.5%, respectively. Nevertheless, burning EHFO could increase the emission of high molecular weight PAHs in fine particles and pose a consequent higher carcinogenic risk for individual particles. The total average (gaseous plus particulate) ΣBEQ of EHFO exhausts (41.5 µg/m3) was generally higher than that of HFO exhausts (18.7 µg/m3). Additionally, the combustion of EHFO (extra-heavy fuel oil) can significantly alter the emission quantity, composition, and particle-size distribution of PAH derivatives. These changes may be linked to molecular structures, such as zigzag configurations in C=O bonds. Our findings may favor the comprehensive environmental assessments on the onboard application of EHFO.

Using the Multicomponent Aerosol FORmation Model (MAFOR) to Determine Improved VOC Emission Factors in Ship Plumes.

International shipping's particulate matter primary emissions have a share in global anthropogenic emissions of between 3% and 4%. Ship emissions of volatile organic compounds (VOCs) can play an important role in the formation of fine particulate matter. Using an aerosol box model for the near-plume scale, this study investigated how the changing VOC emission factor (EF) for ship engines impacts the formation of secondary PM2.5 in ship exhaust plumes that were detected during a measurement campaign. The agreement between measured and modeled particle number size distribution was improved by adjusting VOC emissions, in particular of intermediate-, low-, and extremely low-volatility compounds. The scaling of the VOC emission factor showed that the initial emission factor, based on literature data, had to be multiplied by 3.6 for all VOCs. Information obtained from the box model was integrated into a regional-scale chemistry transport model (CTM) to study the influence of changed VOC ship emissions over the Mediterranean Sea. The regional-scale CTM run with adjusted ship emissions indicated a change in PM2.5 of up to 5% at the main shipping routes and harbor cities in summer. Nevertheless, overall changes due to a change in the VOC EF were rather small, indicating that the size of grid cells in CTMs leads to a fast dilution.

Evaluating infectious disease outbreak potential and mitigation effectiveness on cruise ships.

The cruise ship sector is a major part of the tourism industry, and an estimated over 30 million passengers are transformed worldwide each year. Cruise ships bring diverse populations into proximity for many days, facilitating the transmission of respiratory illnesses. The objective of this study is to develop a modeling framework to inform the development of viable disease risk management policies and measures to control disease outbreaks on cruises. Our model, parameterized and calibrated using the data of the COVID-19 outbreak on the Diamond Princess cruise ship in 2020, is used to assess the impact of the mitigation measures such as mask wearing, vaccination, on-board and pre-traveling testing measures. Our results indicate mask wearing in public places as the cheapest and most affordable measure can drop the number of cumulative confirmed cases by almost 50%. This measure along with the vaccination by declining the number of the cumulative confirmed cases by more than 94% is the most effective measure to control outbreaks on cruises. According to our findings, outbreaks are more predominant in the passenger population than the crew members, however, the protection measures are more beneficial if they are applied by both crew members and passengers. Regarding the testing measure, pre-traveling testing is more functional than the on-board testing to control outbreaks on cruises.

Normative Values for Sternoclavicular Joint and Clavicle Anatomy Based on MR Imaging: A Comprehensive Analysis of 1591 Healthy Participants.

Background: The clavicle remains one of the most fractured bones in the human body, despite the fact that little is known about the MR imaging of it and the adjacent sternoclavicular joint. This study aims to establish standardized values for the diameters of the clavicle as well as the angles of the sternoclavicular joint using whole-body MRI scans of a large and healthy population and to examine further possible correlations between diameters and angles and influencing factors like BMI, weight, height, sex, and age. Methods: This study reviewed whole-body MRI scans from the Study of Health in Pomerania (SHIP), a German population-based cross-sectional study in Mecklenburg-Western Pomerania. Descriptive statistics, as well as median-based regression models, were used to evaluate the results. Results: We could establish reference values based on a shoulder-healthy population for each clavicle parameter. Substantial differences were found for sex. Small impacts were found for height, weight, and BMI. Less to no impact was found for age. Conclusions: This study provides valuable reference values for clavicle and sternoclavicular joint-related parameters and shows the effects of epidemiological features, laying the groundwork for future studies. Further research is mandatory to determine the clinical implications of these findings.

Quantitative evaluation of anthropogenic sources and health risks of rare earth elements in airborne particulate matter.

Rare earth elements (REEs) have emerged as contaminants in airborne particulate matter (PM); however, their anthropogenic sources remain poorly quantified, and associated health risks are unknown. This study investigates the REE distribution across eight sizes of airborne PM during July and December in Qingdao, a major Chinese port city. Our results reveal a single coarse-mode distribution with REE concentrations. In contrast, fine PM (size: 0.43-2.1 µm) exhibits notable enrichment of La and Ce compared to Al and other REEs. This study traces La and Ce enrichment to fluid catalytic cracking catalysts (FCCC)-related sources, including refinery and ship emissions, by comparing the REE fractionation in samples with potential sources. We quantify the contributions from FCCC-related sources to La (July: 33.6 % ± 3.2 %, Dec.: 46.4 % ± 5.2 %) and Ce (July: 16.5 % ± 14.3 %, Dec.: 30.3 % ± 12.2 %) by comparing measured concentrations with predictions derived from neighboring REEs, a method previously used exclusively in aquatic systems. For the first time, supply ratios of refinery and ship to FCCC-related La are calculated using a two-component mixing model based on the [La]FCCC/[V]anth, revealing the dominance of refinery emissions (July: 97.3 % ± 0.6 %, Dec.: 99.6 % ± 0.1 %). Furthermore, a global review of La and Ce anomalies that integrates published REE data with our findings reveals a widespread distribution of positive anomalies. The significantly positive correlation between La and Ce anomalies underscores FCCC-related emissions as a global source in fine PM, contributing 0-92 % (mean: 35 % ± 33 %) for La and 0-72 % (mean: 21 % ± 24 %) for Ce. Although the non-carcinogenic health risks of Ce are generally low globally, concerns should be raised in areas near source emissions, where Ce health risks sharply increased along with its concentrations. There is urgently need to establish a threshold value for La, owing to its global enrichment. This study provides novel insights into the sources and health implications of REEs in airborne PM.

Improving the Flexibility of Ship Propellers Additively Manufactured from High-Density Polyethylene/Long Carbon Fiber Composites by Prepreg Coating.

In efforts to achieve the goal of reducing ship emissions in the fight against climate change, reducing fuel consumption by making ships lighter is stated as one of the solutions. In this study, the possibilities of making composite equivalents of propellers, which are the most complex ship elements and traditionally produced from metal materials, are investigated with the advantages of additive manufacturing, which offers a rapid production opportunity for such forms. In this way, a lighter composite propeller and, therefore, a lighter ship will be achieved, and negative environmental impacts, especially harmful emissions, will be reduced. In the study, a 1/14-scale ship propeller was produced through the material extrusion method of additive manufacturing using an HDPE composite containing long carbon fiber with a 15% weight fraction. An attempt to reduce flexibility with an epoxy-carbon fabric prepreg coating was made, as the flexibility has negative effects on the performance of the produced propeller. The propeller tunnel test showed that the applied carbon fabric epoxy prepreg helped to improve the propeller's performance by decreasing the flexibility of the propeller and reducing the deformation at the tips. At the same time, the propeller weight was decreased by 60% compared to its metal counterparts.